Long-term object tracking supporting autonomous vehicle navigation

ABSTRACT

This disclosure relates in general to systems and methods for tracking objects proximate an autonomous vehicle. In particular, an object tracking system capable of re-identifying objects it has temporarily lost line of sight to is described. Re-identification of the objects allows earlier object detections to be used more effectively to predict motion likely to be taken by the objects.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/954,264, filed Dec. 27, 2019, entitled “LONG-TERMOBJECT TRACKING SUPPORTING AUTONOMOUS VEHICLE NAVIGATION,” the entirecontents of which are hereby incorporated by reference.

FIELD

This description relates to an object tracking system supporting thenavigation of an autonomous vehicle.

BACKGROUND

Autonomous vehicles can be used to transport people and/or cargo (e.g.,packages, objects, or other items) from one location to another. Forexample, an autonomous vehicle can navigate to the location of a person,wait for the person to board the autonomous vehicle, and navigate to aspecified destination (e.g., a location selected by the person). Tonavigate in the environment, these autonomous vehicles are equipped withvarious types of sensors to detect objects in the surroundings.

SUMMARY

While the use of sensors to detect objects proximate the autonomousvehicle is known, improvements in the abilities of the vehicle todetect, continuously track and anticipate future actions of the objectsare desirable. The subject matter described in this specification isdirected to a computer system and techniques for detecting objects in anenvironment surrounding an autonomous vehicle. Generally, the computersystem is configured to receive input from one or more sensors of thevehicle, detect one or more objects in the environment surrounding thevehicle based on the received input, and operate the vehicle based uponthe detection of the objects.

For example, the system can be configured to re-identify objects thathave been temporarily concealed or obscured from one or more sensordetections so that previously detected activity taken by that object canbe used to more accurately predict behavior of that object. An exemplarymethod includes: capturing sensor data using a sensor of an autonomousvehicle; detecting, using a processing circuit, an object from thesensor data, wherein detecting the object includes creating detectiondata; associating, using the processing circuit, the detection data withfirst tracking data; obtaining, using the processing circuit, secondtracking data that meets a stale-track criteria; comparing, using theprocessing circuit, the first tracking data to the second track data;and in accordance with a determination that the comparison of the firsttracking data and the second tracking data meets matching criteria:associating, using the processing circuit, the second tracking data withthe first tracking data; and navigating, using a control circuit, theautonomous vehicle based at least in part on the second tracking data.

These and other aspects, features, and implementations can be expressedas methods, apparatuses, systems, components, program products, means orsteps for performing a function, and in other ways.

These and other aspects, features, and implementations will becomeapparent from the following descriptions, including the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomouscapability.

FIG. 2 illustrates an example “cloud” computing environment.

FIG. 3 illustrates a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by aperception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs andoutputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a controlmodule.

FIG. 12 shows a block diagram of the inputs, outputs, and components ofa controller.

FIG. 13 shows a block diagram of a visual object tracking system.

FIGS. 14A-14C show block diagrams illustrating processing of sensortracks and ground plane tracks according to some embodiments.

FIG. 15 shows a flow chart of the process described generally inreference to a block of FIG. 14B.

FIGS. 16A-16B show exemplary representations of images captured by anobject detection system positioned aboard an autonomous vehicle as theautonomous vehicle approaches a traffic intersection.

FIGS. 17A-17C depict a top view of the intersection depicted in FIGS.16A-16B along with an autonomous vehicle and its object detectionsensor.

FIG. 18 shows a flow chart of a process described generally in referenceto a block of FIG. 14A.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that the disclosed techniques may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thedisclosed techniques.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks and data elements, are shown for ease of description. However, itshould be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

Further, in the drawings, where connecting elements, such as solid ordashed lines or arrows, are used to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not shown in the drawings so as not to obscure thedisclosure. In addition, for ease of illustration, a single connectingelement is used to represent multiple connections, relationships orassociations between elements. For example, where a connecting elementrepresents a communication of signals, data, or instructions, it shouldbe understood by those skilled in the art that such element representsone or multiple signal paths (e.g., a bus), as may be needed, to affectthe communication.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be usedindependently of one another or with any combination of other features.However, any individual feature may not address any of the problemsdiscussed above or might only address one of the problems discussedabove. Some of the problems discussed above might not be fully addressedby any of the features described herein. Although headings are provided,information related to a particular heading, but not found in thesection having that heading, may also be found elsewhere in thisdescription. Embodiments are described herein according to the followingoutline:

-   -   1. General Overview    -   2. Hardware Overview    -   3. Autonomous Vehicle Architecture    -   4. Autonomous Vehicle Inputs    -   5. Autonomous Vehicle Planning    -   6. Autonomous Vehicle Control    -   7. Computing System for Object Detection Using Pillars    -   8. Example Point Clouds and Pillars    -   9. Example Process for Detecting Objects and Operating the        Vehicle Based on the Detection of the Objects

General Overview

Autonomous vehicles driving in complex environments (e.g., an urbanenvironment) pose a great technological challenge. In order forautonomous vehicles to navigate these environments, the vehicles detectvarious types of objects such as vehicles, pedestrians, and bikes inreal-time using sensors such as optical sensors, LIDAR and/or RADAR. Oneapproach to performing object detection utilizes image analysis to trackobjects proximate to an autonomous vehicle.

In some embodiments, the system and techniques described hereinimplement an on-board sensor-based tracking system capable ofdetermining the position of objects surrounding an autonomous vehiclebased on a position of the objects captured by one or more sensorsmounted to or in close proximity to the autonomous vehicle. A positionof the sensor at the time of capture of the images can be known byvirtue of location information provided by a satellite-based navigationreceiver of the autonomous vehicle. When the one or more sensors areoptical sensors and location of the sensor is known at the time ofcapture, the position of an object within an image provides a line ofbearing along which the object is located at the time the image wascaptured. Prior and/or subsequent detection of the object in additionalimages allows for further refinement of where the object is locatedalong the line of bearing. In this way, location information for theobject can be included in an active track that the autonomous vehiclecan reference when navigating the vehicle around the object. It shouldbe noted that other types of sensors such as LiDAR and RADAR can be usedin lieu of or in addition to optical/imagery type sensors.

In addition to identifying the location of objects based on one or moretypes of sensor data, a location of an object can also be refined byreferencing previously captured tracking data. By analyzing previouslycollected tracking data a predicted position of the object can bedetermined. The predicted position can help in a number of ways. First,the predicted position can help to identify which new object detectionsshould be linked with which active track. For example, a vehicletravelling at a high rate of speed relative to the autonomous vehiclecan be located a long distance from its last detected position. Bypredicting a location of the fast-moving object based on its previouslyobserved rate of travel, new tracks can be matched with predictedlocations of the objects to match new and old tracking data. Second,periodic inaccuracies in the tracking data caused by unexpectedvibration of the autonomous vehicle and/or temporary instability of theobject detection sensor can produce image data that varies substantiallyfrom previously collected tracking data. In some embodiments, thedetected position and predicted position can be combined to generate aweighted average of the two positions. This can help to reduce theseverity of transitory errors resulting from problems during sensordetections. In some embodiments, metadata associated with the sensordetections can include inputs from, e.g., a gyroscope or other motiondetection device designed to indicate stabilization issues with thesensor. Third, the predicted position can also be used to help correlatenew tracking data with tracking data associated with an older or staletrack. In this way, historical data associated with an object can bere-identified to help more accurately predict behavior of an object.

In addition to the benefits described above this system benefits frombeing able to be applied to multiple different types of objectsincluding moving objects such as cars and trucks as well as stationaryobjects such as fire hydrants, light polls, buildings and the like. Thedescribed embodiments are not necessarily limited to ground-basedobjects either and could also be applied to vehicles having thecapability to travel above the ground or by sea. It should also be notedthat the described system can be configured to have a stateless dataflowpipeline that makes it easier to run processes in parallel. This isbecause at least in part all the state information associated with thetracking data remains with its associated object track.

Hardware Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomouscapability.

As used herein, the term “autonomous capability” refers to a function,feature, or facility that enables a vehicle to be partially or fullyoperated without real-time human intervention, including withoutlimitation fully autonomous vehicles, highly autonomous vehicles, andconditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possessesautonomous capability.

As used herein, “vehicle” includes means of transportation of goods orpeople. For example, cars, buses, trains, airplanes, drones, trucks,boats, ships, submersibles, dirigibles, etc. A driverless car is anexample of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AVfrom a first spatiotemporal location to a second spatiotemporallocation. In an embodiment, the first spatiotemporal location isreferred to as the initial or starting location and the secondspatiotemporal location is referred to as the destination, finallocation, goal, goal position, or goal location. In some examples, atrajectory is made up of one or more segments (e.g., sections of road)and each segment is made up of one or more blocks (e.g., portions of alane or intersection). In an embodiment, the spatiotemporal locationscorrespond to real world locations. For example, the spatiotemporallocations are pick up or drop-off locations to pick up or drop-offpersons or goods.

As used herein, “sensor(s)” includes one or more hardware componentsthat detect information about the environment surrounding the sensor.Some of the hardware components can include sensing components (e.g.,image sensors, biometric sensors), transmitting and/or receivingcomponents (e.g., laser or radio frequency wave transmitters andreceivers), electronic components such as analog-to-digital converters,a data storage device (such as a RAM and/or a nonvolatile storage),software or firmware components and data processing components such asan ASIC (application-specific integrated circuit), a microprocessorand/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list)or data stream that includes one or more classified or labeled objectsdetected by one or more sensors on the AV vehicle or provided by asource external to the AV.

As used herein, a “road” is a physical area that can be traversed by avehicle, and may correspond to a named thoroughfare (e.g., city street,interstate freeway, etc.) or may correspond to an unnamed thoroughfare(e.g., a driveway in a house or office building, a section of a parkinglot, a section of a vacant lot, a dirt path in a rural area, etc.).Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utilityvehicles, etc.) are capable of traversing a variety of physical areasnot specifically adapted for vehicle travel, a “road” may be a physicalarea not formally defined as a thoroughfare by any municipality or othergovernmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed bya vehicle, and may correspond to most or all of the space between lanemarkings, or may correspond to only some (e.g., less than 50%) of thespace between lane markings. For example, a road having lane markingsspaced far apart might accommodate two or more vehicles between themarkings, such that one vehicle can pass the other without traversingthe lane markings, and thus could be interpreted as having a lanenarrower than the space between the lane markings, or having two lanesbetween the lane markings. A lane could also be interpreted in theabsence of lane markings. For example, a lane may be defined based onphysical features of an environment, e.g., rocks and trees along athoroughfare in a rural area.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact, unless specifiedotherwise.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this description, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array ofhardware, software, stored data, and data generated in real-time thatsupports the operation of the AV. In an embodiment, the AV system isincorporated within the AV. In an embodiment, the AV system is spreadacross several locations. For example, some of the software of the AVsystem is implemented on a cloud computing environment similar to cloudcomputing environment 200 described below with respect to FIG. 2.

In general, this document describes technologies applicable to anyvehicles that have one or more autonomous capabilities including fullyautonomous vehicles, highly autonomous vehicles, and conditionallyautonomous vehicles, such as so-called Level 5, Level 4 and Level 3vehicles, respectively (see SAE International's standard J3016: Taxonomyand Definitions for Terms Related to On-Road Motor Vehicle AutomatedDriving Systems, which is incorporated by reference in its entirety, formore details on the classification of levels of autonomy in vehicles).The technologies described in this document are also applicable topartially autonomous vehicles and driver assisted vehicles, such asso-called Level 2 and Level 1 vehicles (see SAE International's standardJ3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems). In an embodiment, one or more of theLevel 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicleoperations (e.g., steering, braking, and using maps) under certainoperating conditions based on processing of sensor inputs. Thetechnologies described in this document can benefit vehicles in anylevels, ranging from fully autonomous vehicles to human-operatedvehicles.

Referring to FIG. 1, an AV system 120 operates the AV 100 along atrajectory 198 through an environment 190 to a destination 199(sometimes referred to as a final location) while avoiding objects(e.g., natural obstructions 191, vehicles 193, pedestrians 192,cyclists, and other obstacles) and obeying rules of the road (e.g.,rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that areinstrumented to receive and act on operational commands from thecomputer processors 146. In an embodiment, computing processors 146 aresimilar to the processor 304 described below in reference to FIG. 3.Examples of devices 101 include a steering control 102, brakes 103,gears, accelerator pedal or other acceleration control mechanisms,windshield wipers, side-door locks, window controls, andturn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuringor inferring properties of state or condition of the AV 100, such as theAV's position, linear and angular velocity and acceleration, and heading(e.g., an orientation of the leading end of AV 100). Example of sensors121 are GPS, inertial measurement units (IMU) that measure both vehiclelinear accelerations and angular rates, wheel speed sensors formeasuring or estimating wheel slip ratios, wheel brake pressure orbraking torque sensors, engine torque or wheel torque sensors, andsteering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing ormeasuring properties of the AV's environment. For example, monocular orstereo video cameras 122 in the visible light, infrared or thermal (orboth) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight(TOF) depth sensors, speed sensors, temperature sensors, humiditysensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 andmemory 144 for storing machine instructions associated with computerprocessors 146 or data collected by sensors 121. In an embodiment, thedata storage unit 142 is similar to the ROM 308 or storage device 310described below in relation to FIG. 3. In an embodiment, memory 144 issimilar to the main memory 306 described below. In an embodiment, thedata storage unit 142 and memory 144 store historical, real-time, and/orpredictive information about the environment 190. In an embodiment, thestored information includes maps, driving performance, trafficcongestion updates or weather conditions. In an embodiment, datarelating to the environment 190 is transmitted to the AV 100 via acommunications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140for communicating measured or inferred properties of other vehicles'states and conditions, such as positions, linear and angular velocities,linear and angular accelerations, and linear and angular headings to theAV 100. These devices include Vehicle-to-Vehicle (V2V) andVehicle-to-Infrastructure (V2I) communication devices and devices forwireless communications over point-to-point or ad hoc networks or both.In an embodiment, the communications devices 140 communicate across theelectromagnetic spectrum (including radio and optical communications) orother media (e.g., air and acoustic media). A combination ofVehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication(and, in some embodiments, one or more other types of communication) issometimes referred to as Vehicle-to-Everything (V2X) communication. V2Xcommunication typically conforms to one or more communications standardsfor communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communicationinterfaces. For example, wired, wireless, WiMAX, WiFi, Bluetooth,satellite, cellular, optical, near field, infrared, or radio interfaces.The communication interfaces transmit data from a remotely locateddatabase 134 to AV system 120. In an embodiment, the remotely locateddatabase 134 is embedded in a cloud computing environment 200 asdescribed in FIG. 2. The communication interfaces 140 transmit datacollected from sensors 121 or other data related to the operation of AV100 to the remotely located database 134. In an embodiment,communication interfaces 140 transmit information that relates toteleoperations to the AV 100. In some embodiments, the AV 100communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores andtransmits digital data (e.g., storing data such as road and streetlocations). Such data is stored on the memory 144 on the AV 100, ortransmitted to the AV 100 via a communications channel from the remotelylocated database 134.

In an embodiment, the remotely located database 134 stores and transmitshistorical information about driving properties (e.g., speed andacceleration profiles) of vehicles that have previously traveled alongtrajectory 198 at similar times of day. In one implementation, such datamay be stored on the memory 144 on the AV 100, or transmitted to the AV100 via a communications channel from the remotely located database 134.

Computing devices 146 located on the AV 100 algorithmically generatecontrol actions based on both real-time sensor data and priorinformation, allowing the AV system 120 to execute its autonomousdriving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132coupled to computing devices 146 for providing information and alertsto, and receiving input from, a user (e.g., an occupant or a remoteuser) of the AV 100. In an embodiment, peripherals 132 are similar tothe display 312, input device 314, and cursor controller 316 discussedbelow in reference to FIG. 3. The coupling is wireless or wired. Any twoor more of the interface devices may be integrated into a single device.

FIG. 2 illustrates an example “cloud” computing environment. Cloudcomputing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services). Intypical cloud computing systems, one or more large cloud data centershouse the machines used to deliver the services provided by the cloud.Referring now to FIG. 2, the cloud computing environment 200 includescloud data centers 204 a, 204 b, and 204 c that are interconnectedthrough the cloud 202. Data centers 204 a, 204 b, and 204 c providecloud computing services to computer systems 206 a, 206 b, 206 c, 206 d,206 e, and 206 f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud datacenters. In general, a cloud data center, for example the cloud datacenter 204 a shown in FIG. 2, refers to the physical arrangement ofservers that make up a cloud, for example the cloud 202 shown in FIG. 2,or a particular portion of a cloud. For example, servers are physicallyarranged in the cloud datacenter into rooms, groups, rows, and racks. Acloud datacenter has one or more zones, which include one or more roomsof servers. Each room has one or more rows of servers, and each rowincludes one or more racks. Each rack includes one or more individualserver nodes. In some implementation, servers in zones, rooms, racks,and/or rows are arranged into groups based on physical infrastructurerequirements of the datacenter facility, which include power, energy,thermal, heat, and/or other requirements. In an embodiment, the servernodes are similar to the computer system described in FIG. 3. The datacenter 204 a has many computing systems distributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c alongwith the network and networking resources (for example, networkingequipment, nodes, routers, switches, and networking cables) thatinterconnect the cloud data centers 204 a, 204 b, and 204 c and helpfacilitate the computing systems' 206 a-f access to cloud computingservices. In an embodiment, the network represents any combination ofone or more local networks, wide area networks, or internetworks coupledusing wired or wireless links deployed using terrestrial or satelliteconnections. Data exchanged over the network, is transferred using anynumber of network layer protocols, such as Internet Protocol (IP),Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM),Frame Relay, etc. Furthermore, in embodiments where the networkrepresents a combination of multiple sub-networks, different networklayer protocols are used at each of the underlying sub-networks. In someembodiments, the network represents one or more interconnectedinternetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers areconnected to the cloud 202 through network links and network adapters.In an embodiment, the computing systems 206 a-f are implemented asvarious computing devices, for example servers, desktops, laptops,tablet, smartphones, Internet of Things (IoT) devices, autonomousvehicles (including, cars, drones, shuttles, trains, buses, etc.) andconsumer electronics. In an embodiment, the computing systems 206 a-fare implemented in or as a part of other systems.

FIG. 3 illustrates a computer system 300. In an implementation, thecomputer system 300 is a special purpose computing device. Thespecial-purpose computing device is hard-wired to perform the techniquesor includes digital electronic devices such as one or moreapplication-specific integrated circuits (ASICs) or field programmablegate arrays (FPGAs) that are persistently programmed to perform thetechniques, or may include one or more general purpose hardwareprocessors programmed to perform the techniques pursuant to programinstructions in firmware, memory, other storage, or a combination. Suchspecial-purpose computing devices may also combine custom hard-wiredlogic, ASICs, or FPGAs with custom programming to accomplish thetechniques. In various embodiments, the special-purpose computingdevices are desktop computer systems, portable computer systems,handheld devices, network devices or any other device that incorporateshard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or othercommunication mechanism for communicating information, and a hardwareprocessor 304 coupled with a bus 302 for processing information. Thehardware processor 304 is, for example, a general-purposemicroprocessor. The computer system 300 also includes a main memory 306,such as a random-access memory (RAM) or other dynamic storage device,coupled to the bus 302 for storing information and instructions to beexecuted by processor 304. In one implementation, the main memory 306 isused for storing temporary variables or other intermediate informationduring execution of instructions to be executed by the processor 304.Such instructions, when stored in non-transitory storage mediaaccessible to the processor 304, render the computer system 300 into aspecial-purpose machine that is customized to perform the operationsspecified in the instructions.

In an embodiment, the computer system 300 further includes a read onlymemory (ROM) 308 or other static storage device coupled to the bus 302for storing static information and instructions for the processor 304. Astorage device 310, such as a magnetic disk, optical disk, solid-statedrive, or three-dimensional cross point memory is provided and coupledto the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 toa display 312, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), plasma display, light emitting diode (LED) display, or anorganic light emitting diode (OLED) display for displaying informationto a computer user. An input device 314, including alphanumeric andother keys, is coupled to bus 302 for communicating information andcommand selections to the processor 304. Another type of user inputdevice is a cursor controller 316, such as a mouse, a trackball, atouch-enabled display, or cursor direction keys for communicatingdirection information and command selections to the processor 304 andfor controlling cursor movement on the display 312. This input devicetypically has two degrees of freedom in two axes, a first axis (e.g.,x-axis) and a second axis (e.g., y-axis), that allows the device tospecify positions in a plane.

According to one embodiment, the techniques herein are performed by thecomputer system 300 in response to the processor 304 executing one ormore sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory 306 from anotherstorage medium, such as the storage device 310. Execution of thesequences of instructions contained in the main memory 306 causes theprocessor 304 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry is used in place of or incombination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media includes non-volatilemedia and/or volatile media. Non-volatile media includes, for example,optical disks, magnetic disks, solid-state drives, or three-dimensionalcross point memory, such as the storage device 310. Volatile mediaincludes dynamic memory, such as the main memory 306. Common forms ofstorage media include, for example, a floppy disk, a flexible disk, harddisk, solid-state drive, magnetic tape, or any other magnetic datastorage medium, a CD-ROM, any other optical data storage medium, anyphysical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one ormore sequences of one or more instructions to the processor 304 forexecution. For example, the instructions are initially carried on amagnetic disk or solid-state drive of a remote computer. The remotecomputer loads the instructions into its dynamic memory and send theinstructions over a telephone line using a modem. A modem local to thecomputer system 300 receives the data on the telephone line and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector receives the data carried in the infrared signal andappropriate circuitry places the data on the bus 302. The bus 302carries the data to the main memory 306, from which processor 304retrieves and executes the instructions. The instructions received bythe main memory 306 may optionally be stored on the storage device 310either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318coupled to the bus 302. The communication interface 318 provides atwo-way data communication coupling to a network link 320 that isconnected to a local network 322. For example, the communicationinterface 318 is an integrated service digital network (ISDN) card,cable modem, satellite modem, or a modem to provide a data communicationconnection to a corresponding type of telephone line. As anotherexample, the communication interface 318 is a local area network (LAN)card to provide a data communication connection to a compatible LAN. Insome implementations, wireless links are also implemented. In any suchimplementation, the communication interface 318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

The network link 320 typically provides data communication through oneor more networks to other data devices. For example, the network link320 provides a connection through the local network 322 to a hostcomputer 324 or to a cloud data center or equipment operated by anInternet Service Provider (ISP) 326. The ISP 326 in turn provides datacommunication services through the world-wide packet data communicationnetwork now commonly referred to as the “Internet” 328. The localnetwork 322 and Internet 328 both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on the network link 320 and through thecommunication interface 318, which carry the digital data to and fromthe computer system 300, are example forms of transmission media. In anembodiment, the network 320 contains the cloud 202 or a part of thecloud 202 described above.

The computer system 300 sends messages and receives data, includingprogram code, through the network(s), the network link 320, and thecommunication interface 318. In an embodiment, the computer system 300receives code for processing. The received code is executed by theprocessor 304 as it is received, and/or stored in storage device 310, orother non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle(e.g., the AV 100 shown in FIG. 1). The architecture 400 includes aperception module 402 (sometimes referred to as a perception circuit), aplanning module 404 (sometimes referred to as a planning circuit), acontrol module 406 (sometimes referred to as a control circuit), alocalization module 408 (sometimes referred to as a localizationcircuit), and a database module 410 (sometimes referred to as a databasecircuit). Each module plays a role in the operation of the AV 100.Together, the modules 402, 404, 406, 408, and 410 may be part of the AVsystem 120 shown in FIG. 1. In some embodiments, any of the modules 402,404, 406, 408, and 410 is a combination of computer software (e.g.,executable code stored on a computer-readable medium) and computerhardware (e.g., one or more microprocessors, microcontrollers,application-specific integrated circuits [ASICs]), hardware memorydevices, other types of integrated circuits, other types of computerhardware, or a combination of any or all of these things).

In use, the planning module 404 receives data representing a destination412 and determines data representing a trajectory 414 (sometimesreferred to as a route) that can be traveled by the AV 100 to reach(e.g., arrive at) the destination 412. In order for the planning module404 to determine the data representing the trajectory 414, the planningmodule 404 receives data from the perception module 402, thelocalization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using oneor more sensors 121, e.g., as also shown in FIG. 1. The objects areclassified (e.g., grouped into types such as pedestrian, bicycle,automobile, traffic sign, etc.) and a scene description including theclassified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV position418 from the localization module 408. The localization module 408determines the AV position by using data from the sensors 121 and datafrom the database module 410 (e.g., a geographic data) to calculate aposition. For example, the localization module 408 uses data from a GNSS(Global Navigation Satellite System) sensor and geographic data tocalculate a longitude and latitude of the AV. In an embodiment, dataused by the localization module 408 includes high-precision maps of theroadway geometric properties, maps describing road network connectivityproperties, maps describing roadway physical properties (such as trafficspeed, traffic volume, the number of vehicular and cyclist trafficlanes, lane width, lane traffic directions, or lane marker types andlocations, or combinations of them), and maps describing the spatiallocations of road features such as crosswalks, traffic signs or othertravel signals of various types.

The control module 406 receives the data representing the trajectory 414and the data representing the AV position 418 and operates the controlfunctions 420 a-c (e.g., steering, throttling, braking, ignition) of theAV in a manner that will cause the AV 100 to travel the trajectory 414to the destination 412. For example, if the trajectory 414 includes aleft turn, the control module 406 will operate the control functions 420a-c in a manner such that the steering angle of the steering functionwill cause the AV 100 to turn left and the throttling and braking willcause the AV 100 to pause and wait for passing pedestrians or vehiclesbefore the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown inFIG. 1) and outputs 504 a-d (e.g., sensor data) that is used by theperception module 402 (FIG. 4). One input 502 a is a LiDAR (LightDetection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1). LiDARis a technology that uses light (e.g., bursts of light such as infraredlight) to obtain data about physical objects in its line of sight. ALiDAR system produces LiDAR data as output 504 a. For example, LiDARdata is collections of 3D or 2D points (also known as a point clouds)that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that usesradio waves to obtain data about nearby physical objects. RADARs canobtain data about objects not within the line of sight of a LiDARsystem. A RADAR system 502 b produces RADAR data as output 504 b. Forexample, RADAR data are one or more radio frequency electromagneticsignals that are used to construct a representation of the environment190.

Another input 502 c is a camera system. A camera system uses one or morecameras (e.g., digital cameras using a light sensor such as acharge-coupled device [CCD]) to obtain information about nearby physicalobjects. A camera system produces camera data as output 504 c. Cameradata often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). In some examples, the camerasystem has multiple independent cameras, e.g., for the purpose ofstereopsis (stereo vision), which enables the camera system to perceivedepth. Although the objects perceived by the camera system are describedhere as “nearby,” this is relative to the AV. In use, the camera systemmay be configured to “see” objects far, e.g., up to a kilometer or moreahead of the AV. Accordingly, the camera system may have features suchas sensors and lenses that are optimized for perceiving objects that arefar away.

Another input 502 d is a traffic light detection (TLD) system. A TLDsystem uses one or more cameras to obtain information about trafficlights, street signs, and other physical objects that provide visualnavigation information. A TLD system produces TLD data as output 504 d.TLD data often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). A TLD system differs from a systemincorporating a camera in that a TLD system uses a camera with a widefield of view (e.g., using a wide-angle lens or a fish-eye lens) inorder to obtain information about as many physical objects providingvisual navigation information as possible, so that the AV 100 has accessto all relevant navigation information provided by these objects. Forexample, the viewing angle of the TLD system may be about 120 degrees ormore.

In some embodiments, outputs 504 a-d are combined using a sensor fusiontechnique. Thus, either the individual outputs 504 a-d are provided toother systems of the AV 100 (e.g., provided to a planning module 404 asshown in FIG. 4), or the combined output can be provided to the othersystems, either in the form of a single combined output or multiplecombined outputs of the same type (e.g., using the same combinationtechnique or combining the same outputs or both) or different types type(e.g., using different respective combination techniques or combiningdifferent respective outputs or both). In some embodiments, an earlyfusion technique is used. An early fusion technique is characterized bycombining outputs before one or more data processing steps are appliedto the combined output. In some embodiments, a late fusion technique isused. A late fusion technique is characterized by combining outputsafter one or more data processing steps are applied to the individualoutputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 ashown in FIG. 5). The LiDAR system 602 emits light 604 a-c from a lightemitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR systemis typically not in the visible spectrum; for example, infrared light isoften used. Some of the light 604 b emitted encounters a physical object608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Lightemitted from a LiDAR system typically does not penetrate physicalobjects, e.g., physical objects in solid form.) The LiDAR system 602also has one or more light detectors 610, which detect the reflectedlight. In an embodiment, one or more data processing systems associatedwith the LiDAR system generates an image 612 representing the field ofview 614 of the LiDAR system. The image 612 includes information thatrepresents the boundaries 616 of a physical object 608. In this way, theimage 612 is used to determine the boundaries 616 of one or morephysical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown inthis figure, the AV 100 receives both camera system output 504 c in theform of an image 702 and LiDAR system output 504 a in the form of LiDARdata points 704. In use, the data processing systems of the AV 100compares the image 702 to the data points 704. In particular, a physicalobject 706 identified in the image 702 is also identified among the datapoints 704. In this way, the AV 100 perceives the boundaries of thephysical object based on the contour and density of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail.As described above, the AV 100 detects the boundary of a physical objectbased on characteristics of the data points detected by the LiDAR system602. As shown in FIG. 8, a flat object, such as the ground 802, willreflect light 804 a-d emitted from a LiDAR system 602 in a consistentmanner. Put another way, because the LiDAR system 602 emits light usingconsistent spacing, the ground 802 will reflect light back to the LiDARsystem 602 with the same consistent spacing. As the AV 100 travels overthe ground 802, the LiDAR system 602 will continue to detect lightreflected by the next valid ground point 806 if nothing is obstructingthe road. However, if an object 808 obstructs the road, light 804 e-femitted by the LiDAR system 602 will be reflected from points 810 a-b ina manner inconsistent with the expected consistent manner. From thisinformation, the AV 100 can determine that the object 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs andoutputs of a planning module 404 (e.g., as shown in FIG. 4). In general,the output of a planning module 404 is a route 902 from a start point904 (e.g., source location or initial location), and an end point 906(e.g., destination or final location). The route 902 is typicallydefined by one or more segments. For example, a segment is a distance tobe traveled over at least a portion of a street, road, highway,driveway, or other physical area appropriate for automobile travel. Insome examples, e.g., if the AV 100 is an off-road capable vehicle suchas a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-uptruck, or the like, the route 902 includes “off-road” segments such asunpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-levelroute planning data 908. The lane-level route planning data 908 is usedto traverse segments of the route 902 based on conditions of the segmentat a particular time. For example, if the route 902 includes amulti-lane highway, the lane-level route planning data 908 includestrajectory planning data 910 that the AV 100 can use to choose a laneamong the multiple lanes, e.g., based on whether an exit is approaching,whether one or more of the lanes have other vehicles, or other factorsthat vary over the course of a few minutes or less. Similarly, in someimplementations, the lane-level route planning data 908 includes speedconstraints 912 specific to a segment of the route 902. For example, ifthe segment includes pedestrians or un-expected traffic, the speedconstraints 912 may limit the AV 100 to a travel speed slower than anexpected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module 404 includesdatabase data 914 (e.g., from the database module 410 shown in FIG. 4),current location data 916 (e.g., the AV position 418 shown in FIG. 4),destination data 918 (e.g., for the destination 412 shown in FIG. 4),and object data 920 (e.g., the classified objects 416 as perceived bythe perception module 402 as shown in FIG. 4). In some embodiments, thedatabase data 914 includes rules used in planning. Rules are specifiedusing a formal language, e.g., using Boolean logic. In any givensituation encountered by the AV 100, at least some of the rules willapply to the situation. A rule applies to a given situation if the rulehas conditions that are met based on information available to the AV100, e.g., information about the surrounding environment. Rules can havepriority. For example, a rule that says, “if the road is a freeway, moveto the leftmost lane” can have a lower priority than “if the exit isapproaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by theplanning module 404 (FIG. 4). In general, a directed graph 1000 like theone shown in FIG. 10 is used to determine a path between any start point1002 and end point 1004. In real-world terms, the distance separatingthe start point 1002 and end point 1004 may be relatively large (e.g, intwo different metropolitan areas) or may be relatively small (e.g., twointersections abutting a city block or two lanes of a multi-lane road).

In an embodiment, the directed graph 1000 has nodes 1006 a-drepresenting different locations between the start point 1002 and theend point 1004 that could be occupied by an AV 100. In some examples,e.g., when the start point 1002 and end point 1004 represent differentmetropolitan areas, the nodes 1006 a-d represent segments of roads. Insome examples, e.g., when the start point 1002 and the end point 1004represent different locations on the same road, the nodes 1006 a-drepresent different positions on that road. In this way, the directedgraph 1000 includes information at varying levels of granularity. In anembodiment, a directed graph having high granularity is also a subgraphof another directed graph having a larger scale. For example, a directedgraph in which the start point 1002 and the end point 1004 are far away(e.g., many miles apart) has most of its information at a lowgranularity and is based on stored data, but also includes some highgranularity information for the portion of the graph that representsphysical locations in the field of view of the AV 100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannotoverlap with a node. In an embodiment, when granularity is low, theobjects 1008 a-b represent regions that cannot be traversed byautomobile, e.g., areas that have no streets or roads. When granularityis high, the objects 1008 a-b represent physical objects in the field ofview of the AV 100, e.g., other automobiles, pedestrians, or otherentities with which the AV 100 cannot share physical space. In anembodiment, some or all of the objects 1008 a-b are a static objects(e.g., an object that does not change position such as a street lamp orutility pole) or dynamic objects (e.g., an object that is capable ofchanging position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006a-b are connected by an edge 1010 a, it is possible for an AV 100 totravel between one node 1006 a and the other node 1006 b, e.g., withouthaving to travel to an intermediate node before arriving at the othernode 1006 b. (When we refer to an AV 100 traveling between nodes, wemean that the AV 100 travels between the two physical positionsrepresented by the respective nodes.) The edges 1010 a-c are oftenbidirectional, in the sense that an AV 100 travels from a first node toa second node, or from the second node to the first node. In anembodiment, edges 1010 a-c are unidirectional, in the sense that an AV100 can travel from a first node to a second node, however the AV 100cannot travel from the second node to the first node. Edges 1010 a-c areunidirectional when they represent, for example, one-way streets,individual lanes of a street, road, or highway, or other features thatcan only be traversed in one direction due to legal or physicalconstraints.

In an embodiment, the planning module 404 uses the directed graph 1000to identify a path 1012 made up of nodes and edges between the startpoint 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is avalue that represents the resources that will be expended if the AV 100chooses that edge. A typical resource is time. For example, if one edge1010 a represents a physical distance that is twice that as another edge1010 b, then the associated cost 1014 a of the first edge 1010 a may betwice the associated cost 1014 b of the second edge 1010 b. Otherfactors that affect time include expected traffic, number ofintersections, speed limit, etc. Another typical resource is fueleconomy. Two edges 1010 a-b may represent the same physical distance,but one edge 1010 a may require more fuel than another edge 1010 b,e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the startpoint 1002 and end point 1004, the planning module 404 typically choosesa path optimized for cost, e.g., the path that has the least total costwhen the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of acontrol module 406 (e.g., as shown in FIG. 4). A control module operatesin accordance with a controller 1102 which includes, for example, one ormore processors (e.g., one or more computer processors such asmicroprocessors or microcontrollers or both) similar to processor 304,short-term and/or long-term data storage (e.g., memory random-accessmemory or flash memory or both) similar to main memory 306, ROM 308, andstorage device 210, and instructions stored in memory that carry outoperations of the controller 1102 when the instructions are executed(e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing adesired output 1104. The desired output 1104 typically includes avelocity, e.g., a speed and a heading. The desired output 1104 can bebased on, for example, data received from a planning module 404 (e.g.,as shown in FIG. 4). In accordance with the desired output 1104, thecontroller 1102 produces data usable as a throttle input 1106 and asteering input 1108. The throttle input 1106 represents the magnitude inwhich to engage the throttle (e.g., acceleration control) of an AV 100,e.g., by engaging the steering pedal, or engaging another throttlecontrol, to achieve the desired output 1104. In some examples, thethrottle input 1106 also includes data usable to engage the brake (e.g.,deceleration control) of the AV 100. The steering input 1108 representsa steering angle, e.g., the angle at which the steering control (e.g.,steering wheel, steering angle actuator, or other functionality forcontrolling steering angle) of the AV should be positioned to achievethe desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used inadjusting the inputs provided to the throttle and steering. For example,if the AV 100 encounters a disturbance 1110, such as a hill, themeasured speed 1112 of the AV 100 is lowered below the desired outputspeed. In an embodiment, any measured output 1114 is provided to thecontroller 1102 so that the necessary adjustments are performed, e.g.,based on the differential 1113 between the measured speed and desiredoutput. The measured output 1114 includes measured position 1116,measured velocity 1118, (including speed and heading), measuredacceleration 1120, and other outputs measurable by sensors of the AV100.

In an embodiment, information about the disturbance 1110 is detected inadvance, e.g., by a sensor such as a camera or LiDAR sensor, andprovided to a predictive feedback module 1122. The predictive feedbackmodule 1122 then provides information to the controller 1102 that thecontroller 1102 can use to adjust accordingly. For example, if thesensors of the AV 100 detect (“see”) a hill, this information can beused by the controller 1102 to prepare to engage the throttle at theappropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, andcomponents of the controller 1102. The controller 1102 has a speedprofiler 1202 which affects the operation of a throttle/brake controller1204. For example, the speed profiler 1202 instructs the throttle/brakecontroller 1204 to engage acceleration or engage deceleration using thethrottle/brake 1206 depending on, e.g., feedback received by thecontroller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 whichaffects the operation of a steering controller 1210. For example, thelateral tracking controller 1208 instructs the steering controller 1210to adjust the position of the steering angle actuator 1212 depending on,e.g., feedback received by the controller 1102 and processed by thelateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how tocontrol the throttle/brake 1206 and steering angle actuator 1212. Aplanning module 404 provides information used by the controller 1102,for example, to choose a heading when the AV 100 begins operation and todetermine which road segment to traverse when the AV 100 reaches anintersection. A localization module 408 provides information to thecontroller 1102 describing the current location of the AV 100, forexample, so that the controller 1102 can determine if the AV 100 is at alocation expected based on the manner in which the throttle/brake 1206and steering angle actuator 1212 are being controlled. In an embodiment,the controller 1102 receives information from other inputs 1214, e.g.,information received from databases, computer networks, etc.

Object Tracking Architecture

FIG. 13 shows a block diagram describing an object tracking system 1300implemented, for example, in AV system 120. Object detector 1302 is usedto capture and perform analysis on sensor data that can include one ormore images of an area surrounding an autonomous vehicle (e.g., AV 100)or non-imaging data such as RADAR or LIDAR data covering the areasurrounding the autonomous vehicle. The sensor data can be captured by acapture device of object detector 1302, such as a high speed camera,video camera (e.g., camera 122), LIDAR system or RADAR system. Thecapture device will generally be configured to cover a wide field ofview in order to minimize a number of capture devices needed to trackobjects proximate the autonomous vehicle. In some embodiments, thecapture device can take the form of a wide field of view imaging devicewith a fixed focus distance and depth of field well-suited for trackingobjects proximate the autonomous vehicle. Object detector 1302 can havea processing circuit that includes one or more processors 146 thatanalyze the sensor data to identify and classify detected objects (seedescriptions of processors 146 and 304 in the text accompanyingrespective FIGS. 1 and 3). Visual trackers can be configured to performanalysis on any object contained within one or more images captured bythe capture device, while non-imaging sensors can be configured toperform analysis based on sensor-based metrics such as detected movementand/or shape. In some embodiments, processor 146 can be configured toexecute a classification routine stored in, e.g., main memory 306 orother storage media (e.g. storage device 310). The classificationroutine can be a correlation filter tracker, deep tracker and/or Kalmanfilter operative to perform the identification and/or classificationprocess. The classification routine can be configured to distinguishbetween a stationary object, such as a light pole, fire hydrant orbuilding, from a mobile object such as a car, truck, motorcycle orpedestrian. Objects classified as stationary and positioned well outsideof a planned movement path of the autonomous vehicle (e.g., objectsbeyond a threshold distance from the planned movement path) can besafely ignored while stationary objects within or proximate the plannedmovement path can be tracked. Objects determined to be in motion andobjects classified as mobile objects, whether in motion or stationarycan be passed as detection data to a tracker 1304 for further analysisand tracking when the objects are within a threshold distance from theplanned path of movement. The detection data when derived from multipleimagery frames can take many forms and include metrics such as an objectposition, speed, orientation, angular velocity, closing velocity and thelike. Different types of detection data can affect the thresholddistance for any particular object. For example, the threshold distancemay depend on a direction and speed of travel of the object and maychange over time as the direction and speed of travel of the objectchange.

Tracker 1304 is configured to generate sensor tracks from the detectiondata. Tracker 1304 may require multiple detections to generate sensortracks that accurately predicting a rate at which the detected objectslikely traverses a field of view of the sensor. For example, an opticalsystem could generate sensor tracks taking the form of image planetracks relative to objects position within an image frame, while anon-imaging sensor could generate tracks in terms of azimuth andelevation with respect to the sensor or the frame of reference could bemeasured with respect to an orientation of AV 100. In some embodiments,the detection data along with position data of the autonomous vehicle atthe time of each image capture can be broadly referred to as trackingdata. Sensor tracks generated by tracker 1304 with their accompanyingtracking data are forwarded on to tracking and fusion engine 1306.

Tracking and fusion engine 1306 includes one or more processing circuitsconfigured to convert the sensor tracks to ground plane tracks. Whilethe conversion of sensor tracks to ground plane tracks can be a usefulintermediary step, it should be appreciated that in some embodiments,the creation of ground plane tracks can instead be performed in a singlestep in which detection data and map data are combined to form a groundplane track. Ground plane tracks describe a location of the detectedobject with respect to the surface of the earth rather than a locationrelated to the sensor. To convert the sensor tracks to ground planetracks, a location of the sensor tracks within the sensor field of viewand the location of the autonomous vehicle at the time of each of theimage captures can be used to provide a line of bearing between theautonomous vehicle and corresponding detected objects. Determining aprecise distance separating the autonomous vehicle can be difficult todetermine from the imagery-based data with high levels of precision. Insome embodiments, the distance information can be based on correlatingimage cues in the image frame surrounding the detected object with knownlocations associated with previously captured imagery. For example, aposition of an object having the appearance of an automobile, offsetlaterally from the autonomous vehicle and located between lane markerscan be determined to a reasonable level of certainty by determining anintersection of the line of bearing with the lane defined by lanemarkers visible in the imagery. Higher levels of lateral offset andcloser ranges make this type of determination more accurate as an anglebetween the lane and the line of bearing derived from the images islarger. Proximity of the tracked object to visually distinct featuressuch as traffic signals and other discernable objects within the imageframe can also help to determine a distance between a particular objectand the autonomous vehicle. In some embodiments, distance informationcan be determined by a separate range sensor such as a RADAR, LiDAR orinfrared sensor. With certain configurations of the ranging sensor, theranging sensor may only be directed to those areas corresponding tolocations in which objects are visually detected.

After using the bearing and determined range information to convert thesensor tracks to ground plane tracks, tracking and fusion system 1306can add additional prediction information to the ground plane tracksbased on, e.g. map data to more accurately determine likely behavior ofthe object based on the objects position with respect to the map data.The map data may include information such as which direction a vehiclecan turn at a particular intersection, thereby limiting the number oflikely directions of travel that will be taken by a vehicle located in aparticular lane. Behavior prediction can take many forms and rely onmany different factors which will be discussed in greater detail below.Tracking and fusion system 1306 can forward the ground plane tracks andprediction information to an autonomous navigation system 1308 to helpautonomous navigation system 1306 determine a path for the autonomousvehicle (e.g., to avoid the detected objects). In some embodiments,autonomous navigation system 1306 includes a control circuit that isresponsible for directing navigation of the autonomous vehicle.

In some embodiments, the functions of tracker 1304 and tracking andfusion system 1306 can be combined allowing the creation of a singletype of track allowing the generation of an intermediate track type tobe skipped.

FIG. 14A shows a block diagram 1400 illustrating a more detaileddescription of how tracker 1304 converts detection data into sensortracks. In particular, detection data is transmitted from objectdetector 1302 to track generator 1402. Detection data can take manyforms including, a range between the autonomous vehicle and the object,a rate of change of the range, an angular orientation of the object withrespect to the autonomous vehicle and a velocity of the object. Trackgenerator 1402 converts the detection data into sensor tracks. Thesensor track can include at least a detected position of each objectidentified by object detector 1302. In cases where the detection data isgenerated from more than one frame of imagery, additional metrics suchas velocity or frame crossing rate can be determined from any motion ofthe object across the image frame apparent from the multiple frames ofimagery. In some embodiments, 5-10 image frames can be analyzed at atime. For example, by analyzing 5-10 images at a time from an opticalsensor that collects 60 images per second as many as 6-12 track analysescould be performed per second with a system that considers every imagecollected. Slower rates of, e.g., 5-10 images per second are alsopossible to ease processing burdens and in situations in which few to nonearby objects are projected to interfere with a planned route of travelof the autonomous vehicle. In some embodiments a sampling speed can varybased on the existence of any perceived objects positioned in a mannerthat could result in an imminent collision. It should be noted thatsensor capture can also vary greatly across different sensor types. Forexample, as appreciated by individual having an ordinary level of skillin the art, non-imaging sensors can have sensor readings at a muchhigher rate of speed than imagery sensors since a detection event for anon-imaging sensor can take the form of a single photon or reflectedradio wave.

Track generator 1402 then generates sensor tracks, which include atminimum detected positions of the detected objects with respect to thesensor at a particular time and assigns the same track ID to each pieceof data associated with each sensor track. The generated sensor tracksare then transmitted to correlation engine 1408. Tracker 1304 alsoreceives active track data from track data storage 1404. Track datastorage 1404 is shown as a subcomponent of tracking and fusion system1306 but in some embodiments, track data storage 1404 can be separatefrom tracking and fusion system 1306. Track data storage 1404 caninclude both sensor tracks and ground plane tracks. In some embodiments,the track data can be organized in track files or objects that caninclude both object position relative to the sensor and ground planeposition information. The active track data transmitted to tracker 1304by track data storage 1404 can be processed by prediction engine 1406.Prediction engine 1406 can be configured to determine a likely positionof each object included in the active track data. Because the activetrack data can include ground track data, prediction engine 1406 canalso benefit from any prediction information stored in the active trackdata that was derived from map data restricting possible movement of theobject at a particular point of time later than the newest tracking dataincluded in the active track data.

Prediction engine 1406 outputs a sensor track that includes at least apredicted position of the objects. Matching engine 1408 compares thepredicted positions of the objects derived from active track data withthe detected position of the objects from the image tracks generated bytrack generator 1402. In some embodiments, additional metrics such asdirection and speed of travel, object shape, object color and the likecan be included in the comparison. The comparison helps matching engine1408 to identify which of the detected positions correlate withpredicted positions generated from the active track data provided bytrack data storage 1404. In some embodiments, any sensor tracks that donot correlate with a predicted position of the active tracks will beforwarded on to tracking and fusion system 1306 as a new track withoutany historical tracking data. Those image track files with detectedpositions that do correlate to a predicted position can be combined withdata from the active track. Combination of the data can includeassigning the same track ID to all of the combined tracking data. Insome embodiments, the predicted and detected positions of the object canbe combined by averaging the positions together. The way in which thepredicted and detected positions are averaged together can differ basedon various factors such as a level of confidence in the tracking dataassociated with the active track as well as a level of confidence in thequality of the object detection data.

FIG. 14B shows a block diagram 1410 that includes the same componentsworking in the same manner as described in FIG. 14A with the exceptionof track data storage 1404 being configured to provide stale track datato matching engine 1408. Stale tracks are those tracks that have not yetbeen deleted for being too old and/or have a confidence level that failsto meet a particular threshold value. Stale tracks stay in memory withintrack data storage until they are correlated with new tracking data andmeet an active track classification criteria or are deleted. Active andstale-track criteria are generally distinguished from one another basedon factors such as tracking data age, object position with respect tothe autonomous vehicle and tracking data confidence. For example, atrack could be considered to be an active track when tracking data ofthe track has been updated within a threshold period of time and/or isassigned a threshold tracking data confidence level based on theposition and/or direction of travel of the object associated with thetrack. The stale-track criteria can also be based at least in part on anumber of image frames captured without detection of the object. In someembodiments, tracking data confidence level can also be based on atemporal consistency of the data making up the tracking data. Thetemporal consistency of the data being a measure of how closely thesensor data used to form the second tracking data follows expectedtrends.

Given that in some embodiments, no predicted position is calculated forstale tracks, a matching criteria for stale track data can be basedprimarily upon a size and shape of the object. Orientation of the objectrelative to the sensor can also be a consideration. For example, adetected object having the same color but a different size and/or shapemight still be matched if a detected orientation and/or position changeis deemed to have caused the change in size/shape. In this case, thesize and/or shape data can be disregarded or these factors can at leastbe weighted less heavily in the matching criteria. The matching criteriafor the stale track data may also be applied only after trying to findan active track that matches one of the newly generated sensor tracks.In some embodiments, position data associated with the most recentdetection or detections that are included in the stale track data canalso be used as a factor in determining a likelihood of the stale trackdata being associated with the same object as the newly generated sensortrack. For example, if the object would have had to accelerate ordecelerate at an improbable rate to arrive at the newly detectedposition the correlation to the previously detected object could bedeemed to have failed a matching criteria. When combining stale trackdata with newly generated sensor tracks, matching engine 1408 willgenerally use the detected position in lieu of trying to average thedetected position with any predicted position information associatedwith the stale track data. As described with the active trackcombination of old and new tracking data, the old and new data can becombined by assigning all the tracking data the same track ID. It shouldbe noted that in some embodiments, prediction engine 1406 can beconfigured to receive the stale track data prior to matching engine 1408to allow prediction engine 1406 to generate predicted position data forstale track data having particularly high confidence levels. Forexample, older position data for a parked car that had been detectedturning on its lights could still have a high confidence level evenwhere the age of the sale track would normally be disqualified forprediction, thereby allowing stale track data to be used to help refinea position of the parked car deemed to be likely to move soon.

FIG. 14C shows a block diagram illustrating additional detail regardinga different specific implementation for the processing of sensor tracksby tracking and fusion system 1306. In particular, FIG. 14C illustrateshow tracking and fusion system 1306 combines newly gathered trackingdata with historical tracking data instead of tracker 1304 as describedin preceding FIGS. 14A-14B. At 1422, tracking and fusion system 1306 isconfigured to convert new sensor tracks to ground plane tracks aspreviously described in the text accompanying FIG. 13. At 1424 and 1426,the new ground plane tracks are compared with older active and staletracks, respectively. Stale tracks are those tracks that have not yetbeen deleted for being too old and/or unreliable but also fail to meetan active track classification criteria. Active and stale-track criteriaare generally distinguished from one another based on factors such astracking data age, object position with respect to the autonomousvehicle and tracking data confidence. For example, a track could beconsidered to be an active track when tracking data has been updatedwithin a threshold period of time and/or is assigned a thresholdtracking data confidence level based on the position and/or direction oftravel of the object associated with the track. The stale-track criteriacan also be based at least in part on a number of image frames capturedwithout detection of the object. In some embodiments, tracking dataconfidence level can also be based on a temporal consistency of the datamaking up the tracking data.

In particular, at 1424, newly converted ground plane tracks are comparedwith active tracks. The new ground plane tracks that meet a matchingcriteria with an older active track are fused with the matching activetrack at 1428 to form a single updated active track. In someembodiments, the tracking data from the older active track is fused withtracking data from the new track by changing a track ID of the newtracking data to a track ID of the older active track. In this way, thesensor data associated with the new ground plane track can be combinedwith the older tracking data so that autonomous navigation system 1308is able to use both current and historical data to more easily predictfuture movement of the object. In some embodiments, a track ID oftracking data associated with the older active track can be changed tohave the track ID associated with the new tracking data. Matchingcriteria between the new tracking data and old tracking data can varybased on the operating conditions of the autonomous vehicle but is basedgenerally on the new ground plane tracks being proximate to an expectedor projected location of the object based on the historical trackingdata associated with the active tracks.

Tracking and fusion system 1306 can optionally include at 1426, aprocess in which remaining unmatched ground plane tracks are comparedwith older tracks having a stale status. This allows some objects thatmay have been temporarily obscured by another object, sun glare,illumination variation due to shadowing/clouds, or the like to bere-identified. At 1430, the unmatched ground plane tracks that meet amatching criteria with one of the older stale tracks are fused with datafrom the stale track to generate a single updated active track that isforwarded on to autonomous navigation system 1308. In this way, the newsensor data can be combined with the older tracking data so thatautonomous navigation system 1308 is able to use both current andhistorical data to better predict future movement of the object.Matching criteria can vary based on the operating conditions of theautonomous vehicle but is based generally on a new ground plane trackbeing located near an expected location of the object based onhistorical ground plane tracks. It should be noted that matching staletracks can be more problematic to perform reliably since the staletracks typically have gone without update for at least a short period oftime making reliable prediction more difficult. Consequently, thematching criteria for fusion of current tracks with older stale trackscan be configured more conservatively than the matching criteria foractive tracks to avoid mismatching. In this way, the new sensor data canbe reliably combined with the older tracking data so that autonomousnavigation system 1308 is able to use both current and historical datato more easily predict future behavior of the object in situations inwhich one or more of the objects goes undetected for brief periods oftime.

When there are new ground plane tracks that do not match any of theolder active or stale tracks, at 1432, the unmatched ground plane trackscan be forwarded on to autonomous navigation system 1308 without anyhistorical tracking data for use in predicting behavior to assist withnavigating the autonomous vehicle.

FIG. 15 shows a flow chart illustrating a method for associating a newlydetected object with a track meeting a stale track criteria, describedgenerally at block 1408 of FIG. 14B. At 1502, a new sensor track thatincludes first tracking data, not having already been associated with anactive track, is compared with second tracking data that is included ina track meeting a stale-track criteria by a processing circuit executinginstructions stored on a storage medium (e.g., main memory 306 orstorage device 310). It should be noted that the active and stale trackmatching processes performed at block 1408 can also be performedconcurrently, in which case even the new sensor tracks eventuallyassociated with an active track would also be compared against tracksmeeting a stale-track criteria prior to a final correlation being made.At 1504, the comparison of the first and second tracking data can beused by the processing circuit to determine whether the first and secondtracking data share similar features sufficient to meet a matchingcriteria. In some embodiments the matching criteria can be used todetermine whether differences in the tracking data associated with thenew sensor track and stale track are consistent with a period of timethat has elapsed between a time of collection of the most recent dataassociated with the stale track and the tracking data used to generatethe new sensor track.

At 1506, tracking data from the matching tracks can be merged togetherto create an updated active sensor track containing the information fromboth the first and second tracking data. In some embodiments, apredicted position of the object generated from tracking data of thestale track can be combined with the detected position of the objectfrom the new sensor track to improve the accuracy of the object. Thistype of combination would typically be performed when the stale trackhad just recently become stale and/or where due to the object detectionsystem tracking multiple objects, obscuration of the object by anothertracked object was expected to occur and variation between the detectedposition and predicted position are within expected tolerances. Aftermerging the tracking data into an active sensor track, the sensor trackis forwarded on to detection and tracking system 1306 where the activesensor track is converted to a ground plane track and then provided tothe autonomous navigation system 1308, which allows a control circuit touse the tracking data included in the ground plane track to navigate theautonomous vehicle. In some embodiments, the tracking data from theground plane track can be used to help the autonomous vehicle inavoiding the object associated with the updated active track. It shouldbe appreciated that the method described in relation to FIG. 15 can alsobe applied to block 1430 from FIG. 14C with minor modification.

FIGS. 16A-16B show images captured by an object detection systempositioned aboard an autonomous vehicle as it approaches a trafficintersection. In particular, FIG. 16A shows how vehicle 1602 is beingtracked by the object detection system. A rectangular indicia 1604indicates an active track being used to monitor the activity of vehicle1602. The object detection system is also tracking vehicle 1606, whichis shown by rectangular indicia 1608. While numerous other vehicles areshown in this scene, for purposes of clarity the movement of onlyvehicles 1602 and 1606 are discussed. Active tracks associated with bothvehicles 1602 and 1604 can be well established at this point and includeat least multiple seconds worth of tracking data.

FIG. 16B shows how vehicle 1606 re-emerges after passing behind vehicle1602. When a vehicle is obscured in this manner a track associated withthe vehicle can transition from an active track to a stale track incertain circumstances. In this particular example, because vehicle 1606became obscured at the entry to an intersection, extrapolation of itsmovement became uncertain enough for the associated track to be markedas stale due to the uncertainty of its trajectory. For example, vehicle1606 could proceed through the intersection, remain stopped at theintersection or begin turning right at the intersection while vehicle1606 is obscured. While vehicle obscuration is one reason for the systemto lose track of an object other factors can also result in theinability of the object detection system to keep track of a particularobject. For example, sun glare could also prevent continuous tracking ofvehicle 1606. Other reasons for tracking loss includes deformation ofthe object due to distortion inherent in the lens of the optical sensorthat prevents the tracking system from continuing to consider the objectbeing deformed by lens distortion from being the same object.

When vehicle 1606 re-emerges from out behind vehicle 1602, the fact thatits position is consistent with one of its possible routes of travel(remaining stationary) and its color and shape are consistent with thecolor and shape seen in previously captured images, allows the objectdetection system to associate the position information of vehicle 1606extrapolated from the image illustrated in FIG. 16B with tracking datagathered on vehicle 1606 prior to it being obscured by vehicle 1602. Itshould be appreciated from the depicted images shown in FIGS. 16A and16B that in certain cases the changing background shapes behind vehicle1606 can make certain identification of vehicle 1606 more difficult. Inthe depicted example, as vehicle 1602 proceeds through the trafficintersection, different portions of building 1610 would be positionedbehind vehicle 1606 changing the background behind vehicle 1606. Where acertain vehicle feature was mistaken for a feature of the car thischange could potentially affect the ability of the object identificationsystem to re-associate old tracking data with vehicle 1606.

In some embodiments, activity of the traffic light captured by theobject detection system or an independent traffic light detection systemof the autonomous vehicle could also be considered in projectingmovement of vehicle 1606. For example, once the former tracking data isre-identified with vehicle 1606, its stationary position at theintersection can make it much less likely that vehicle 1606 intends tomake a right hand turn at an intersection where right hand turns on redsignals are authorized. In this way, the object detection system couldanticipate vehicle 1606 will continue to remain stationary at theintersection until the traffic light changes state at which pointvehicle 1606 could proceed through the intersection or across theintersection with a left hand turn.

FIGS. 17A-17C depict a top view of the intersection depicted in FIGS.16A-16B along with autonomous vehicle 1702 and optical sensor 1704. Inparticular, FIGS. 17A and 17C correspond to the images depicted inrespective FIGS. 16A and 16B. As previously described, the position ofvehicles 1602 and 1606 in the images illustrated in FIGS. 16A and 16Bcan be converted into ground plane tracks, as depicted in FIGS. 17A-17B.Conversion of the imagery data into location-based tracking data allowsthe autonomous vehicle to more accurately predict and avoid otherobjects it is tracking by correlating the position of the objects withmap data. Fusion of the tracking data with map data allows for even morecertainty as to direction, speed and likely behavior as the map includesinformation such as speed limit, number and position of lanes, positionand operation of traffic signals, position of parking spaces and thelike. For example, a vehicular object detected in a location known to bea parking spot can be presumed to be maintaining its position and beunlikely to move from its position.

FIG. 17B shows an intermediate position of vehicles between the imagesillustrated in FIGS. 16A and 16B. In particular, FIG. 17B shows howvehicle 1602 can obscure vehicle 1606 from view as vehicle 1602 pullsfarther forward of autonomous vehicle 1702. This type of objectobscuration can be referred to as object occlusion. As previouslydiscussed, without a clear line of sight to vehicle 1606, the objectdetection system at this point can only guess as to what actions vehicle1606 is taken while stopped. In FIG. 17C, vehicle 1602 no longerobscures a line of sight view of vehicle 1606 allowing the objectdetection system to reacquire vehicle 1606.

FIG. 18 shows a flow chart illustrating additional detail of the processperformed and previously described in FIG. 14A at block 1408. Inparticular, associating the matching sensor tracks and active tracksincludes at 1802, predicting a first position of the object at time T₀using tracking data from the matching active track. Since the trackingdata associated with the active track can include position datacollected over the course of multiple frames, this predicted positioncan be more accurate than the detected position due to the potential forinaccuracies in the measurement of a single position. At 1804, a secondposition of the object extracted from the sensor data captured at timeT₀ can be obtained from the tracking data making up the recentlyconverted sensor track. At 1806, instead of registering the secondposition of the object as the position of the object at Time To,registering a position of the object at Time To as a weighted average ofthe second position and the first position. The weighting of thisaverage can vary based on a number of factors including the consistencyof the historical data, the quality of the sensor data captured at timeT₀ and other factors affecting whether the predicted or detectedposition is considered likely to be more accurate of an actual positionof the object at Time To. It should be noted that while the detectionand tracking system described in FIG. 18 is effective that inalternative embodiments, new tracking data could include only thedetected position without factoring in the predicted position prior toupdating the tracking data for the ground plane track.

In the foregoing description, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The description and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the claims, and what is intendedby the applicants to be the scope of the claims, is the literal andequivalent scope of the set of claims that issue from this application,in the specific form in which such claims issue, including anysubsequent correction. Any definitions expressly set forth herein forterms contained in such claims shall govern the meaning of such terms asused in the claims. In addition, when we use the term “furthercomprising,” in the foregoing description or following claims, whatfollows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A method, comprising: capturing sensor data usinga sensor of an autonomous vehicle; detecting, using a processingcircuit, an object from the sensor data, wherein detecting the objectincludes creating detection data; associating, using the processingcircuit, the detection data with first tracking data; obtaining, usingthe processing circuit, second tracking data that meets a stale-trackcriteria; comparing, using the processing circuit, the first trackingdata to the second track data; and in accordance with a determinationthat the comparison of the first tracking data and the second trackingdata meets matching criteria: associating, using the processing circuit,the second tracking data with the first tracking data; and navigating,using a control circuit, the autonomous vehicle based at least in parton the second tracking data.
 2. The method of claim 1, wherein thedetection data includes at least one of a detected range, range rate,angular position, or velocity.
 3. The method of claim 1, wherein thefirst tracking data is assigned a first track ID.
 4. The method of claim3, wherein associating, using the processing circuit, the secondtracking data with the first tracking data includes: assigning thesecond tracking data the first track ID.
 5. The method of claim 1,further comprising predicting a future position of the object based atleast in part on the first tracking data and the second tracking data.6. The method of claim 5, further comprising: capturing additionalsensor data; generating new tracking data from the additional sensordata; and in accordance with a determination that the new tracking dataincludes a position of the object that corresponds to the predictedfuture position of the object, assigning the new tracking data a trackID assigned to the first tracking data.
 7. The method of claim 1,wherein the stale-track criteria is based at least in part on trackingdata age.
 8. The method of claim 7, wherein tracking data confidence ofthe second tracking data is based at least in part on a temporalconsistency of the data making up the second tracking data.
 9. Themethod of claim 1, wherein the first tracking data further comprises atleast one of a velocity of the object, a shape of the object, a size ofthe object, a color of the object, or an orientation of the object. 10.The method of claim 1, wherein the second tracking data meets thestale-track criteria due to the occurrence of at least one of objectocclusion, illumination variation, or deformation.
 11. The method ofclaim 1, wherein the sensor data includes imagery and creating thedetection data includes extracting visual features from portions of theimagery that include the object.
 12. The method of claim 1, wherein thesensor is a fixed focus imaging sensor.
 13. The method of claim 1,wherein the sensor is one of a LiDAR sensor and a RADAR sensor.
 14. Themethod of claim 1, wherein the tracking data is generated by at leastone of a correlation filter tracker, a deep tracker, or a Kalman filtertracker.
 15. The method of claim 1, wherein the stale-track criteria ismet when the most recent data associated with the tracking data is olderthan a first threshold period of time.
 16. The method of claim 15,further comprising: in accordance with a determination that the mostrecent data associated with the tracking data is older than a secondthreshold period of time that is greater than the first threshold periodof time, deleting tracking data categorized as being stale.
 17. Themethod of claim 1, wherein the comparison meets the matching criteriawhen at least one of an object shape, size, or position included in thefirst tracking data matches a shape, size, or position included in thesecond tracking data.
 18. The method of claim 17, wherein: in accordancewith a determination that differences in the first tracking data and thesecond tracking data are due to a change in orientation of the objectwith respect to the sensor, comparing the first tracking data to thesecond tracking data disregards the differences in the first trackingdata and the second tracking data that are due to the change inorientation of the object with respect to the sensor.
 19. Anon-transitory computer-readable storage medium storing instructionsconfigured to be executed by one or more circuits of a computing devicethat cause the computing device to carry out steps that include:capturing sensor data using a sensor of an autonomous vehicle;detecting, using a processing circuit, an object from the sensor data,wherein detecting the object includes creating detection data;associating, using the processing circuit, the detection data with firsttracking data; obtaining, using the processing circuit, second trackingdata that meets a stale-track criteria; comparing, using the processingcircuit, the first tracking data to the second track data; and inaccordance with a determination that the comparison of the firsttracking data and the second tracking data meets a matching criteria:associating, using the processing circuit, the second tracking data withthe first tracking data; and navigating, using a control circuit, theautonomous vehicle based at least in part on the second tracking data.20. An autonomous vehicle, comprising: a sensor; a processing circuit; acontrol circuit; and memory storing one or more programs configured tobe executed by the circuits of the autonomous vehicle, the one or moreprograms including instructions for: capturing sensor data using thesensor; detecting, using the processing circuit, an object from thesensor data, wherein detecting the object includes creating detectiondata; associating, using the processing circuit, the detection data withfirst tracking data; obtaining, using the processing circuit, secondtracking data that meets a stale-track criteria; comparing, using theprocessing circuit, the first tracking data to the second track data;and in accordance with a determination that the comparison of the firsttracking data and the second tracking data meets a matching criteria:associating, using the processing circuit, the second tracking data withthe first tracking data; and navigating, using the control circuit, theautonomous vehicle based at least in part on the second tracking data.